Gender Recognition Using Nonsubsampled Contourlet Transform and WLD Descriptor
Conference paper
Abstract
Gender recognition using facial images plays an important role in biometric technology. Multiscale texture descriptors perform better in gender recognition because they encode the multiscale facial microstructures in a better way. We present a gender recognition system that uses SVM, two-stage feature selection and multiscale texture feature based on Nonsubsampled Contourlet Transform and Weber law descriptor (NSCT-WLD). The proposed system has better recognition rate (99.50%) than the state-of-the-art methods on FERET database. This research also reveals that in NSCT decomposition what is essential for face recognition and what is important for other tasks like age detection.
Keywords
Gender recognition Face recognition WLD Descriptor Nonsubsampled Contourlet Transform Support Vector MachinesPreview
Unable to display preview. Download preview PDF.
References
- 1.Zang, J., Lu, B.L.: A support vector machine classifier with automatic confidence and its application to gender classification. Neurocomputing 74, 1926–1935 (2011)CrossRefGoogle Scholar
- 2.Moghaddam, B., Yang, M.-H.: Gender classification with support vector machines. In: Proc. IEEE International Conference on Automatic Face and Gesture Recognition, pp. 306–311 (March 2000)Google Scholar
- 3.Gutta, S., Wechsler, H., Phillips, P.: Gender and ethnic classification of face images. In: Third IEEE International Conference on Automatic Face and Gesture Recognition (FG 1998), pp. 194–199 (1998)Google Scholar
- 4.Ullah, I., Hussain, M., Aboalsamh, H., Muhammad, G., Mirza, A.M., Bebis, G.: Gender Recognition from Face Images with Dyadic Wavelet Transform and Local Binary Pattern. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Fowlkes, C., Wang, S., Choi, M.-H., Mantler, S., Schulze, J., Acevedo, D., Mueller, K., Papka, M. (eds.) ISVC 2012, Part II. LNCS, vol. 7432, pp. 409–419. Springer, Heidelberg (2012)CrossRefGoogle Scholar
- 5.Baluja, S., Rowley, H.: Boosting sex identification performance. International Journal of Computer Vision 71(1), 111–119 (2007)CrossRefGoogle Scholar
- 6.Lu, L., Shi, P.: Fusion of multiple facial regions for expression-invariant gender classification. IEICE Electronic Express 6(10), 587–593 (2009)CrossRefGoogle Scholar
- 7.Alexandre, L.A.: Gender recognition: A multiscale decision fusion approach. Pattern Recognition Letters 31, 1422–1427 (2010)CrossRefGoogle Scholar
- 8.Zhou, J., Cunha, A.L., Do, M.N.: Nonsubsampledcontourlet transform: construction and application in enhancement. In: Proc. ICIP 2005, pp. I 469-72 (2005)Google Scholar
- 9.Chen, J., Shan, S., He, C., Zhao, G., Pietikainen, M., Chen, X., Gao, W.: WLD: A robust local image descriptor. IEEE TPAMI 32(9), 1705–1720 (2010)CrossRefGoogle Scholar
- 10.Hart, P.E., Duda, R.O., Stork, D.G.: Pattern Classification. Wiley-Interscience Publication (2001)Google Scholar
- 11.Sun, Y., Todorovic, S., Goodison, S.: Local-learning-based feature selection for high-dimensional data analysis. IEEE TPAMI 32(9), 1610–1626 (2010)CrossRefGoogle Scholar
- 12.Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The FERET evaluation methodology for face-recognition algorithms. IEEE TPAMI 22(10), 1090–1104 (2000)CrossRefGoogle Scholar
- 13.Veropoulos, K., Bebis, G., Webster, M.A.: Investigating the impact of face cate-gorization on recognition performance. In: Bebis, G., Boyle, R., Koracin, D., Parvin, B. (eds.) ISVC 2005. LNCS, vol. 3804, pp. 207–218. Springer, Heidelberg (2005)CrossRefGoogle Scholar
Copyright information
© Springer-Verlag Berlin Heidelberg 2013